What the data for this category looks like
Chemical pharmaceutical intelligent due diligence reports serve scenarios such as pharmaceutical track investment for financial institutions, and underwriting and risk assessment for insurance institutions targeting pharmaceutical companies. Data mainly comes from public documents from national drug regulatory review centers, annual financial reports of pharmaceutical companies, clinical trial public platforms, patent databases and industry compliance archives. Update frequency adjusts based on source channels: review-related documents are updated monthly, and financial reports are updated annually. Documents are a mix of structured and semi-structured data, including fields such as compound CAS numbers, production process parameters, clinical trial sample sizes, and approval document numbers. Units include professional pharmaceutical units such as mass concentration, number of cases, and purity percentage.
What constraints do these characteristics impose on deployment and upgrade
The mixed structured format and specialized field requirements of chemical pharmaceutical due diligence data for financial and insurance scenarios require deployment to adapt to long document parsing and custom field extraction, to avoid losing core information such as CAS numbers and process parameters. Differences in update rhythms across multiple channels require upgrade to support incremental synchronization, to ensure that running due diligence tasks are not interrupted. For intranet deployment scenarios, local parsing plugins and vector storage must be configured to avoid relying on external network interfaces, to meet the intranet security requirements of financial institutions. Specialized unit and field verification logic must be preset during deployment, to avoid unit confusion or missing fields in subsequent parsing results that affect the compliance of due diligence reports.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | Chemical pharmaceutical documents are mostly long PDFs or Excel files with structural formulas, which take longer to parse. 900 seconds covers parsing for complex files |
maxChunkSize | 8000–12000 characters | Chemical pharmaceutical documents include long paragraphs of process descriptions and clinical data. Too long a chunk will lose context, too short will fail to associate complete logic. This range adapts to semantic integrity of professional documents |
RECALL_RERANK_TOP_N | Top 8 entries | Due diligence reports need to cover multi-dimensional compliance data and process parameters. Too many recalled entries will increase context pressure. 8 entries balances recall coverage and inference efficiency |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Pharmaceutical company financial reports and large clinical data documents have large file sizes. This upper limit covers conventional batch upload requirements |
SYNC_INCREMENTAL_ENABLE | Enabled | Chemical pharmaceutical data is updated in batches across different channels. Incremental synchronization avoids excessive server resource usage from full synchronization, while ensuring data timeliness |
VECTOR_STORE_BATCH_SIZE | 64 entries | Vector generation for professional documents requires significant computing resources. This batch size balances upload speed and server load |
The parameter values provided on this page are general recommendations to serve as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: When
maxChunkSizeis set to 5000 andmaxContextis set to 1500, the retrieval result still returns complete chunks. Cause: Filter rules to limit the context length of recalled content are not configured at the same time, resulting in the retrieval phase not truncating overlong chunks. - Phenomenon: Only the fastgpt container image is upgraded, and the dependent parsing plugin image is not updated at the same time, resulting in failure to correctly extract structural formulas in chemical pharmaceutical documents. Cause: There is a version binding relationship between fastgpt and the parsing plugin. Upgrading fastgpt alone will cause plugin compatibility failure.
- Phenomenon: After intranet deployment, local stored XLSX format process documents cannot be read, and the parsing result is empty. Cause: The local file storage path and permissions are not configured, and the local file access permission of the intranet parsing plugin is not enabled.
How to confirm the configuration is correct
- Upload a chemical pharmaceutical PDF document containing CAS numbers and process parameters, and check whether core fields are fully extracted in the parsing result, with no missing content or garbled text.
- Run an incremental synchronization task, and check that the synchronization log only displays newly added or updated document entries, with no full synchronization markers.
- Adjust the
maxChunkSizeandmaxContextparameters, retrieve the test document, and check that the returned context length meets the preset upper limit. - Restart the fastgpt service and parsing plugin, and check that there are no version compatibility error messages in the container logs.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.